Working papers results

After the creation of the European Monetary Union (EMU), both the European Commission (EC) and the European Central Bank (ECB) are focusing more and more on the evolution of the EMU as a whole, rather than on single member countries. A particularly relevant issue from a policy point of view is the availability of reliable forecasts for the key macroeconomic variables. Hence, both the fiscal and the monetary authorities have developed aggregate forecasting models, along the lines previously adopted for the analysis of single countries. A similar approach will be likely followed in empirical analyses on, e.g., the existence of an aggregate Taylor rule or the evaluation of the aggregate impact of monetary policy shocks, where linear specifications are usually adopted. Yet, it is uncertain whether standard linear models provide the proper statistical framework to address these issues. The process of aggregation across countries can produce smoother series, better suited for the analysis with linear models, by averaging out country specific shocks. But the method of construction of the aggregate series, which often involves time-varying weights, and the presence of common shocks across the countries, such as the deflation in the early 1980s and the convergence process in the early 1990s, can introduce substantial non-linearity into the generating process of the aggregate series. To evaluate whether this is the case, we fit a variety of non-linear and time-varying models to aggregate EMU macroeconomic variables, and compare them with linear specifications. Since non-linear models often over-fit in sample, we assess their performance in a real time forecasting framework. It turns out that for several variables linear models are beaten by non-linear specifications, a result that questions the use of standard linear methods for forecasting and modeling EMU variables.

 

 

The aim of this paper is to estimate the effect of research externalities across space, in generating innovation.We do so by using R&D and patent data for eighty-six European Regions in the 1977-1995 period. We find that spillovers exist for regions within a distance of 300 Km from each other. The estimates are robust to simultaneity, omitted variable bias, different specifications of distance functions, country and border effects. The size of these spillovers is small, though. Doubling R&D spending in a region would increase the output of new ideas in other regions within 300 Km only by 2-3%, while it would increase the innovation of the region itself by 80-90%. Given the small size and the limited range of diffusion, we interpret these externalities as the result of local diffusion of non-codified knowledge, embodied in people and spreading via personal contacts. This interpretation is reinforced by the finding that the spillovers are somewhat weaker across national borders.

The objective of this study is to investigate the behaviour of monetary and fiscal authorities in the Euro area. Our main contribution is joint modelling of behaviour of the two authorities. Our investigation highlights a number of facts. The systematic monetary policies adopted by the non-German authorities in the seventies were not capable of stabilizing inflation. Such results have been achieved in the eigthies and the nineties by anchoring more tightly domestic monetary policy to German monetary policy. All the main episodes of expansionary fiscal policy occurred in the course of the eigthies and the nineties in Europe cannot be explained by the sytematic behaviour of fiscal authorities. Stabilization of inflation has been achieved independently from the lack of fiscal discipline. There are important interactions between the two authorities but they depend exclusively on the responses of governemnts expenditures and receipts to interest rate payments on the public debt.

Revised version: June 28, 2002

Despite the fast catching-up in ICT diffusion experienced by most EU countries in the last few years, information technologies have so far delivered little productivity gains in Europe. In the second half of the past decade, the growth contributions from ICT capital rose in six EU countries only (the UK, Denmark, Finland, Sweden, Ireland and Greece). Quite unlike the United States, this has not generally been associated to higher labour or total factor productivity growth rates, the only exceptions being Ireland and Greece. Particularly worrisome, the large countries in Continental Europe (Germany, France, Italy and Spain) showed stagnating or mildly declining growth contributions from ICT capital, together with definite declines in TFP growth compared to the first half of the 1990s. It looks like that the celebrated Solow paradox on the lack of correlation between ICT investment and productivity growth has fled the US to migrate to Europe.

It is rather common to have several competing forecasts for the same variable, and many methods have been suggested to pick up the best, on the basis of their past forecasting performance. As an alternative, the forecasts can be combined to obtain a pooled forecast, and several options are available to select what forecasts should be pooled, and how to determine their relative weights. In this paper we compare the relative performance of alternative pooling methods, using a very large dataset of about 500 macroeconomic variables for the countries in the European Monetary Union. In this case the forecasting exercise is further complicated by the short time span available, due to the need of collecting a homogeneous dataset. For each variable in the dataset, we consider 58 forecasts produced by a range of linear, time-varying and non-linear models, plus 16 pooled forecasts. Our results indicate that on average combination methods work well. Yet, a more disaggregate analysis reveals that single non-linear models can outperform combination forecasts for several series, even though they perform rather badly for other series so that on average their performance is not as good as that of pooled forecasts. Similar results are obtained for a subset of unstable series, the pooled forecasts behave only slightly better, and for three key macroeconomic variables, namely, industrial production, unemployment and inflation.

In this paper we evaluate the relative performance of linear, non-linear and time-varying models for about 500 macroeconomic variables for the countries in the Euro area, using real-time forecasting methodology. It turns out that linear models work well for about 35% of the series under analysis, time-varying models for another 35% and on-linear models for the remaining 30% of the series. The gains in forecasting accuracy from the choice of the best model can be substantial, in particular for longer forecast horizons.These results emerge from a detailed is aggregated analysis, while they are hidden when an average loss function is used. To explore in more detail the issue of parameter instability, we then apply a battery of tests, detecting non-constancy in about 20-30% of the time series. For these variables the forecasting performance of the time-varying and non-linear models further improves, with larger gains for a larger fraction of the series. Finally, we evaluate whether non-linear models perform better for three key macroeconomic variables: industrial production, inflation and unemployment. It turns out that this is often the case. Hence, overall, our results indicate that there is a substantial amount of instability and non-linearity in the EMU, and suggest that it can be worth going beyond linear models for several EMU macroeconomic variables.

This paper introduces Heckscher-Ohlin trade features into a two-country DSGE model, and studies the international transmission of productivity shocks through trade in goods. This framework improves upon existing international real business cycle models in generating business cycle properties comparable with the empirical evidence concerning the terms of trade and the trade balance.

Index tracking requires to build a portfolio of stocks (a replica) whose behavior is as close as possible to that of a given stock index. Typically, much fewer stocks should appear in the replica than in the index, and there should be no low frequency (persistent) components in the tracking error. Unfortunately, the latter property is not satisfied by many commonly used methods for index tracking. These are based on the in-sample minimization of a loss function, but do not take into account the dynamic properties of the index components. Instead, we represent the index components with a dynamic factor model, and develop a procedure that, in a first step, builds a replica that is driven by the same persistent factors as the index. In a second step, it is also possible to refine the replica so that it minimizes a loss function, as in the traditional approach. Both Monte Carlo simulations and an application to the EuroStoxx50 index provide substantial support for our approach.